Search Results for author:
Found 11 papers, 2 papers with code
Neural Continuous-Time Markov Models
The Gillespie algorithm is used to generate trajectories of stochastic systems where propensity functions (reaction rates) are known.
Drift Identification for Lévy alpha-Stable Stochastic Systems
This paper focuses on a stochastic system identification problem: given time series observations of a stochastic differential equation (SDE) driven by L\'{e}vy $\alpha$-stable noise, estimate the SDE's drift field.
Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System
We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension.
Equity-Directed Bootstrapping: Examples and Analysis
When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, e. g., one.
Statistical learning method for predicting density-matrix based electron dynamics
We develop a statistical method to learn a molecular Hamiltonian matrix from a time-series of electron density matrices.
Estimating Vector Fields from Noisy Time Series
We also study the combination of either our neural shape function method or existing differential equation learning methods with alternating minimization and multiple trajectories.
A non-autonomous equation discovery method for time signal classification
We view the time signal as a forcing function for a dynamical system that governs a time-evolving hidden variable.
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics
As a more rigorous test, we use the learned Hamiltonians to simulate electron dynamics in the presence of an applied electric field, extrapolating to a problem that is beyond the field-free training data.
Learning and Interpreting Potentials for Classical Hamiltonian Systems
We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential.
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models.
Predicting Adolescent Suicide Attempts with Neural Networks
Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide.
